LOD-PCAC: Level-of-Detail-Based Deep Lossless Point Cloud Attribute Compression.

Area of Science:

• Computer Science
• Data Compression
• Machine Learning

Background:

• Efficient compression of point cloud attributes is crucial for handling large datasets.
• Existing deep learning methods excel in lossy compression, but lossless compression remains a challenge.
• Voxel-based compression methods struggle with sparse or uneven point cloud densities.

Purpose of the Study:

• To develop a novel learning-based framework for lossless point cloud attribute compression.
• To address the limitations of existing methods in handling varying point cloud densities.
• To improve the efficiency and robustness of point cloud attribute compression.

Main Methods:

• Introduced a Level-of-Detail (LOD) structure to divide point clouds into multiple detail levels.
• Constructed a Reference Set using vertices from different detail levels to capture multi-level information.
• Proposed a Bit-level Residual Coder that predicts attribute values and organizes residual bits into a Bit Matrix for context.

Main Results:

• The proposed LOD-PCAC framework achieves density-robust lossless compression for point cloud attributes.
• Experimental results show superior performance compared to traditional and learning-based approaches.
• The method demonstrates strong generalization across different datasets and point cloud densities.

Conclusions:

• LOD-PCAC offers an effective solution for lossless point cloud attribute compression, particularly for sparse or uneven data.
• The Level-of-Detail structure and Bit-level Residual Coder are key innovations for robust compression.
• The framework advances the state-of-the-art in point cloud data compression.